Optimizing Long-Term Robot Tracking With Multi-Platform Sensor Fusion

Giuliano Albanese, Arka Mitra, Jan-Nico Zaech, Yupeng Zhao, Ajad Chhatkuli, Luc Van Gool; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024, pp. 6992-7002

Abstract

Monitoring a fleet of robots requires stable long-term tracking with re-identification, which is yet an unsolved challenge in many scenarios. One application of this is the analysis of autonomous robotic soccer games at RoboCup. Tracking in these games requires handling of identically looking players, strong occlusions, and non-professional video recordings, but also offers state information estimated by the robots. In order to make effective use of the information coming from the robot sensors, we propose a robust tracking and identification pipeline. It fuses external non-calibrated camera data with the robots' internal states using quadratic optimization for tracklet matching. The approach is validated using game recordings from previous RoboCup World Cup tournaments.

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[bibtex]
@InProceedings{Albanese_2024_WACV, author = {Albanese, Giuliano and Mitra, Arka and Zaech, Jan-Nico and Zhao, Yupeng and Chhatkuli, Ajad and Van Gool, Luc}, title = {Optimizing Long-Term Robot Tracking With Multi-Platform Sensor Fusion}, booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}, month = {January}, year = {2024}, pages = {6992-7002} }